therapeutic protein) PK using PBPK modeling, due to uncertainties in parameters related to target-mediated disposition (TMD) and the neonatal Fc receptor (FcRn)-mediated recycling pathway [5]. implemented in the drug development process. Keywords:Monoclonal antibodies, pharmacokinetics, physiologically-based pharmacokinetics, drug development, drug discovery == INTRODUCTION == Monoclonal antibody (mAb)-based therapeutics rank among the best-selling and fastest growing classes of therapeutics available on the market, with indications in many significant disease areas, including cancer, autoimmune diseases, infectious diseases, and cardiovascular disease [1,2]. These molecules are highly attractive drug candidates due to their high affinity and specificity for a target of interest. However, their high affinity for target molecules can be a double-edged sword, as interaction with target can often lead to non-linear pharmacokinetics (PK), which generally is not well-predicted using interspecies scaling approaches such as allometry. Accurate projection of human pharmacokinetics and pharmacodynamics (PK/PD) using in vitro and preclinical in vivo data Mouse monoclonal to Tyro3 would be useful in facilitating translation Harmaline to clinical studies with a high probability of success. One approach that could be used in a translational setting would be scaling of mechanism-based PK/PD models from preclinical species to man; however, to date, reported model-based scaling efforts have had mixed results in the prediction of the clinical PK of mAbs. The use of mechanism-based mathematical models to guide drug development, termed model-based drug development (MBDD), has been identified as an approach to improve decision-making throughout the drug discovery/development timeline, from lead compound selection through clinical trial design [3]. Recently, Hu and Hansen have highlighted the role that MBDD has taken in the development of antibody therapeutics, describing the utility of various types of models throughout the development timeline [4]. In their review, the authors discuss model-based approaches for target identification, lead optimization, human PK prediction, and optimization of dosing regimens; however, they suggest that different types of models may be optimal at different stages of development [4]. In an Harmaline ideal scenario, a model developed at the target identification stage of drug discovery could be scaled and extended to provide utility throughout the entire development process, incorporating knowledge as it is gained. Physiologically-based pharmacokinetic (PBPK) models are a platform that has become prevalent in recent years to predict the in vivo behavior of small molecule drugs, partially due to commercially available software for PBPK such as Simcyp and GastroPlus. The use Harmaline of PBPK models represents an attractive approach for prediction of plasma and tissue pharmacokinetics (PK), as they are able to integrate knowledge across Harmaline various levels, from the macroscopic (anatomical) space all the way down to the molecular level (protein-protein interactions). A recent review that summarized applications of PBPK in the pharmaceutical industry highlighted prediction of cytochrome P450 (CYP)-mediated clearance, CYP-based drug-drug interactions (DDIs), and prediction of food effects on absorption as areas where PBPK is routinely used in drug development [5]. In that same review, it was stated that there is only low-to-moderate confidence in the prediction of large molecule (e.g. therapeutic protein) PK using Harmaline PBPK modeling, due to uncertainties in parameters related to target-mediated disposition (TMD) and the neonatal Fc receptor (FcRn)-mediated recycling pathway [5]. However, despite potential uncertainty in key parameters, there have been several predictive PBPK models of antibody disposition published that are able to well-describe the time course of drug exposure. While the limitations highlighted in the aforementioned review do present a hurdle in the development of predictive PBPK models for antibodies, published models have used best guess approximations and assumptions based on available information in the literature to facilitate model-building efforts. PBPK has been utilized for mAbs for over 30 years, and a brief summary of select publications in this field can be found inTable 1. The first PBPK model developed for antibodies was described in 1986 by Covell and colleagues, and it included 6 tissues (lung, gut, liver, spleen, kidney, carcass) divided into 3 layers (capillary plasma,.